New Internship Opportunity @

In conjunction withSpringer Nature,DBpedia offers a3 months internship at Springer Nature in London, UK and at DBpedia in Leipzig, Germany.

Internship Details

Position

DBpedia Intern

Main Employer

DBpedia Association

Deadline

June 30th, 2017

Duration

3 months/full-time, internship will starts in the second half of 2017

Location

50% in London (UK) and 50% in Leipzig (GER)

Type of students desired

Undergraduate, Graduate (Junior role)

Compensation

You will receive a stipend of 1300€ per month and additional reimbursement of your travel and visa costs (total up to 1000€)

The student intern will be responsible for assisting with mappings for DBpedia at SpringerNature. Your tasks include and are not restricted to improving the quality of the extraction mechanism of DBpedia scholarly references/wikipedia citations to Springer Nature URIs and Text mining of DBpedia entities from Springer Nature publication content.

GSoC students have finally been selected.

We are very excited to announce this year’s final students for our projects at the Google Summer of Code program (GSoC).

Google Summer of Code is a global program focused on bringing more student developers into open source software development. Stipends are awarded to students to work on a specific DBpedia related project together with a set of dedicated mentors during summer 2017 for the duration of three months.

For the past 5 years DBpedia has been a vital part of the GSoC program. Since the very first time many Dbpedia projects have been successfully completed.

In this years GSoC edition, DBpedia received more than 20 submissions for selected DBpedia projects. Our mentors read many promising proposals, evaluated them and now the crême de la crême of students snatched a spot for this summer. In the end 7 students from around the world were selected and will jointly work together with their assigned mentors on their projects. DBpedia developers and mentors are really excited about this 7 promising student projects.

You want to read more about their specific projects? Just click below… or check GSoC pages for details.[expander_maker id=”1″ more=”Read more” less=”Read less”] Ismael Rodriguez– Project Description: Although the DBPedia Extraction Framework was adapted to support RML mappings thanks to a project of last year GSoC, the user interface to create mappings is still done by a MediaWiki installation, not supporting RML mappings and needing expertise on Semantic Web. The goal of the project is to create a front-end application that provides a user-friendly interface so the DBPedia community can easily view, create and administrate DBPedia mapping rules using RML. Moreover, it should also facilitate data transformations and overall DBPedia dataset generation. Mentors: Anastasia Dimou, Dimitris Kontokostas, Wouter Maroy

Ram Ganesan Athreya – Project Description:The requirement of the project is to build a conversational Chatbot for DBpedia which would be deployed in at least two social networks.There are three main challenges in this task. First is understanding the query presented by the user, second is fetching relevant information based on the query through DBpedia and finally tailoring the responses based on the standards of each platform and developing subsequent user interactions with the Chatbot.Based on my understanding, the process of understanding the query would be undertaken by one of the mentioned QA Systems (HAWK, QANARY, openQA). Based on the response from these systems we need to query the DBpedia dataset using SPARQL and present the data back to the user in a meaningful way. Ideally, both the presentation and interaction flow needs to be tailored for the individual social network.I would like to stress that although the primary medium of interaction is text, platforms such as Facebook insist that a proper mix between chat and interactive elements such as images, buttons etc would lead to better user engagement. So I would like to incorporate these elements as part of my proposal.

Mentor: Ricardo Usbeck

Nausheen Fatma – Project discription:Knowledge base embeddings has been an active area of research. In recent years a lot of research work such as TransE, TransR, RESCAL, SSP, etc. has been done to get knowledge base embeddings. However none of these approaches have used DBpedia to validate their approach. In this project, I want to achieve the following tasks: i) Run the existing techniques for KB embeddings for standard datasets. ii) Create an equivalent standard dataset from DBpedia for evaluations. iii) Evaluate across domains. iv) Compare and Analyse the performance and consistency of various approaches for DBpedia dataset along with other standard datasets. v)Report any challenges that may come across implementing the approaches for DBpedia. Along the way, I would also try my best to come up with any new research approach for the problem.

Mentors: Sandro Athaide Coelho, Tommaso Soru

Akshay Jagatap – Project Description: The project aims at defining embeddings to represent classes, instances and properties. Such a model tries to quantify semantic similarity as a measure of distance in the vector space of the embeddings. I believe this can be done by implementing Random Vector Accumulators with additional features in order to better encode the semantic information held by the Wikipedia corpus and DBpedia graphs.

Mentors: Pablo Mendes, Sandro Athaide Coelho, Tommaso Soru

Luca Virgili – Project Description: In Wikipedia a lot of data are hidden in tables. What we want to do is to read correctly all tables in a page. First of all, we need a tool that can allow us to capture the tables represented in a Wikipedia page. After that, we have to understand what we read previously. Both these operations seem easy to make, but there are many problems that could arise. The main issue that we have to solve is due to how people build table. Everyone has a particular style for representing information, so in some table we can read something that doesn’t appear in another structure. In this paper I propose to improve the last year’s project and to create a general way for reading data from Wikipedia tables. I want to review the parser for Wikipedia pages for trying to understand more types of tables possible. Furthermore, I’d like to build an algorithm that can compare the column’s elements (that have been read previously by the parser) to an ontology so it could realize how the user wrote the information. In this way we can define only few mapping rules, and we can make a more generalized software.

Mentors: Emanuele Storti, Domenico Potena

Shashank Motepalli – Project Description: DBpedia tries to extract structured information from Wikipedia and make information available on the Web. In this way, the DBpedia project develops a gigantic source of knowledge. However, the current system for building DBpedia Ontology relies on Infobox extraction. Infoboxes, being human curated, limit the coverage of DBpedia. This occurs either due to lack of Infoboxes in some pages or over-specific or very general taxonomies. These factors have motivated the need for DBTax.DBTax follows an unsupervised approach to learning taxonomy from the Wikipedia category system. It applies several inter-disciplinary NLP techniques to assign types to DBpedia entities. The primary goal of the project is to streamline and improve the approach which was proposed. As a result, making it easy to run on a new DBpedia release. In addition to this, also to work on learning taxonomy of DBTax to other Wikipedia languages.

Mentors: Marco Fossati, Dimitris Kontokostas

Krishanu Konar – Project Description: Wikipedia, being the world’s largest encyclopedia, has humongous amount of information present in form of text. While key facts and figures are encapsulated in the resource’s infobox, and some detailed statistics are present in the form of tables, but there’s also a lot of data present in form of lists which are quite unstructured and hence its difficult to form into a semantic relationship. The project focuses on the extraction of relevant but hidden data which lies inside lists in Wikipedia pages. The main objective of the project would be to create a tool that can extract information from wikipedia lists, form appropriate RDF triplets that can be inserted in the DBpedia dataset.

Mentor: Marco Fossati [/expander_maker]

Congrats to all selected students! We will keep our fingers crossed now and patiently wait until early September, when final project results are published.

An encouraging note to the less successful students.

The competition for GSoC slots is always on a very high level and DBpedia only has a limited amount of slots available for students. In case you weren’t among the selected, do not give up on DBpedia just yet. There are plenty of opportunities to prove your abilities and be part of the DBpedia experience. You, above all, know DBpedia by heart. Hence, contributing to our support system is not only a great way to be part of the DBpedia community but also an opportunity to be vital to DBpedia’s development. Above all, it is a chance for current DBpedia mentors to get to know you better. It will give your future mentors a chance to support you and help you to develop your ideas from the very beginning.

Go on you smart brains, dare to become a top DBpedia expert and provide good support for other DBpedia Users. Sign up to our support page or check out the following ways to contribute:

Get involved:

Join our DBpedia-discussion-mailinglist, where we discuss current DBpedia developments. NOTE: all mails announcing tools or call to papers unrelated to DBpedia are not allowed. This is a community discussion list.

If you like to join DBpedia developers discussion and technical discussions sign up in Slack

Developer Discussion

Become a DBpedia Student and sign up for free at the DBpedia Association. We offer special programs that provide training and other opportunities to learn about DBpedia and extend your Semantic Web and programming skills

Do you want to stay informed about upcoming DBpedia events, releases and technical developments? Through the DBpedia newsletter you get the possibility to be always up to date and to provide feedback to us. Four times per year we will inform the DBpedia community about meetings, new collaborations and other topics related to DBpedia. So … Continue reading STAY TUNED AND SIGN UP FOR THE DBPEDIA NEWSLETTER→

DBpedia will participate for a fifth time in the Google Summer of Code program (GSoC) and now we are looking for students who will share their ideas with us. We are regularly growing our community through GSoC and can deliver more and more opportunities to you. We got excited with our new ideas, we hope you will get excited too!

What is GSoC?

Google Summer of Code is a global program focused on bringing more student developers into open source software development. Funds will given to students (BSc, MSc, PhD) to work for three months on a specific task. At first open source organizations announce their student projects and then students should contact the mentor organizations they want to work with and write up a project proposal for the summer. After a selection phase, students are matched with a specific project and a set of mentors to work on the project during the summer.

Sören Auer and the DBpedia Board members prepared a survey to assess the direction of the DBpedia Association. We would like to know what you think should be our priorities and how you would like the funds of the association to be used.

Your opinion counts – so please contribute actively in developing a better DBpedia. If you use DBpedia and want us to keep going forward, we kindly invite you to vote here: https://goo.gl/forms/rDqLcwL823Ok09Uw2

We will publish the results in anonymized, aggregated form on the DBpedia website.

As previous years, we would like your input for DBpedia related project ideas for GSoC 2017.

For those who are unfamiliar with GSoC (Google Summer of Code), Google pays students (BSc, MSc, PhD) to work for 3 months on an open source project. Open source organizations announce their student projects and students apply for projects they like. After a selection phase, students are matched with a specific project and a set of mentors to work on the project during the summer.

The DBpedia community and members from over 20 countries work hard to localize and internationalize DBpedia and support the extraction of non-English Wikipedia editions as well as build a data community around a certain language, region or special interest. The chapters are part of the DBpedia executives and have taken on responsibility to contribute to the infrastructure of DBpedia.

Other partners like imec/Ghent University and Institute of Sound and Vision have signed as well and became an executive partner of the DBpedia Association. The Vrije Universiteit will join soon. It is a cooperation between these Dutch organizations as well as the NL-DBpedia community.

After our successful meeting in Poznan in 2015, we thought it is time to meet the Polish DBpedia community again. The DBpedia meetup will be held on 22th of November 2016 at the Poznań University of Economics and Business. This meetup aims at the presentation of semantic web technologies and their use in applications by entrepreneurs.

After the largest DBpedia meeting to date we decided it was time to cross the Atlantic for the second time for another meetup. Two weeks ago the 8th DBpedia Community Meeting was held in Sunnyvale, California on October 27th 2016.

Main Event

Pablo Mendes from Lattice Data Inc. opened the main event with a short introduction setting the tone for the evening. After that Dimitris Kontokostas gave technical and organizational DBpedia updates. The main event attracted attendees with lightning talks from major companies actively using DBpedia or interested in knowledge graphs in general.

Four major institutions described their efforts to organize reusable information in a centralized knowledge representation. Google’s Tatiana Libman presented (on behalf of Denny Vrandečić) the impressive scale of the Google Knowledge graph, with 1B+ entities and over 100 billion facts.

Tatiana Libman from Google

Yahoo’s Nicolas Torzec presented the Yahoo knowledge graph, with focus on their research on extracting data from Web tables to expand their knowledge which includes DBpedia as an important part. Qi He from LinkedIn focused mostly on how to model a knowledge graph of people and skills, which becomes particularly interesting with the possibility of integration with Microsoft’s Satori Graph. Such an integration would allow general domain knowledge and very specific knowledge about professionals complementing one another. Stas Malyshev from Wikidata presented statistics on their growth, points of contact with DBpedia as well as an impressive SPARQL query interface that can be used to query the structured data that they are generating.

Three other speakers focused on the impact of DBpedia in machine learning and natural language processing. Daniel Gruhl from IBM Watson gave the talk “Truth for the impatient” where he showed that a knowledge model built from DBpedia can help costs and time to value for extracting entity mentions with higher accuracy. Pablo Mendes from Lattice Data Inc. presented their approach that leverages DBpedia and other structured information sources for weak supervision to obtain very strong NLP extractors. Sujan Perera from IBM Watson discussed the problem of identifying implicit mentions of entities in tweets and how the knowledge represented in DBpedia can be used to help uncover those references.

Another three speakers focused on applications of DBpedia and knowledge graphs. Margaret Warren from Metadata Authoring Systems, LLC presented ImageSnippets and how background knowledge from DBpedia allows better multimedia search through inference. For instance, by searching for “birds” you may find pictures that haven’t been explicitly tagged as birds but for which the fact can be inferred from DBpedia. Jans Aasman from Franz Inc presented their company’s approach to Data Exploration with Visual SPARQL Queries. They described opportunities for graph analytics in the medical domain, and discussed how DBpedia has been useful in their applications. Finally, Wang-Chiew Tan presented their research at RIT relating to building chatbots, among other projects that relate to using background knowledge stored in computers to enrich real life experiences.

Nicolas Torzec from Yahoo

Overall the talks were very high quality and fostered plenty of discussions afterwards. We finalized the event with a round of introductions where every attendee got to say their name and affiliation to help them connect with one another throughout the final hour of the event.

All slides and presentations are also available on ourWebsite and you will find more feedback and photos about the event on Twitter via #DBpediaCA.

Less than 24 hours left to reserve your seat for our 2nd US DBpedia Community meeting. The meeting will be held in Sunnyvale on October 27th 2016, hosted by Yahoo. Over 85 participants registered so far, we will offer 20 more tickets. So come and get your ticket to be part of this event.

The event will feature talks from Yahoo, IBM Watson, LinkedIn, Lattice, Wikimedia, Frank Inc, Knoesis, RIT and ImageSnippets. The topics will include knowledge graphs & machine learning, open data, open source and startups. Please read below on different ways you can participate. We are looking forward to meeting again in person with the US-based DBpedia community.